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Glama

Kegg

by io.github.pipeworx-io

Find

find
Read-onlyIdempotent

Search KEGG by keyword. KEGG is the authoritative bioinformatics database for compounds, drugs, diseases, metabolic pathways, genes, and enzymes. Pick a database (compound|drug|disease|pathway|genes|enzyme|glycan|module|ko) and pass a query like "glucose", "aspirin", or "diabetes". Returns matching KEGG IDs with descriptions. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch term, e.g. "glucose", "aspirin", "diabetes".
databaseYesOne of: compound, drug, disease, pathway, genes, enzyme, glycan, module, ko.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "database": "compound",
+    "query": "glucose"
+  },
+  {
+    "database": "drug",
+    "query": "aspirin"
+  },
+  {
+    "database": "disease",
+    "query": "diabetes"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it is 'keyless' (no API key) and returns KEGG IDs with descriptions, providing extra context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences plus a standalone 'Keyless.' which is efficient. Each sentence serves a purpose: purpose, domain context, examples, and auth info. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool is a simple search with 2 parameters and no output schema, the description explains the database options, query examples, and the return format (IDs with descriptions). It is sufficiently complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description reinforces this with concrete examples and a list of database values, making parameter usage clearer.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Search KEGG by keyword' and provides specific examples of databases and queries, making the purpose unmistakable and distinct from sibling tools like get_entry or search_within.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly lists acceptable databases and gives example queries, guiding when to use. However, it lacks explicit 'when not to use' or alternatives, though the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation3/5

Most tools have clearly described distinct purposes, but several overlap: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all occupy neighboring query/discovery territory. The Polymarket and memory tool families, by contrast, are well differentiated.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes verb-first names (ask_pipeworx, compare_entities, resolve_entity, validate_claim) with noun-phrase names (entity_profile, bet_research, recent_alerts, polymarket_arbitrage) and brand prefixes (pipeworx_*, polymarket_*). There is no consistent verb_noun pattern across the toolkit.

Tool Count2/5

34 tools is well beyond the heavy range, and the count is especially inappropriate because the server is named Kegg but only find, get_entry, and list_database relate to KEGG bioinformatics. The remaining 31 tools span unrelated domains (generic data research, prediction markets, memory, subscriptions, AI visibility, npm scanning), making the scope feel like several products merged into one.

Completeness2/5

As a KEGG server, the surface is severely thin: three read-only tools with no pathway mapping, sequence search, or cross-reference utilities. The Pipeworx research and Polymarket betting subsystems are more complete, but their presence under a Kegg server makes the overall surface incoherent rather than complete.

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